IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v222y2022ics0951832022000977.html
   My bibliography  Save this article

Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints

Author

Listed:
  • He, Xinxin
  • Wang, Zhijian
  • Li, Yanfeng
  • Khazhina, Svetlana
  • Du, Wenhua
  • Wang, Junyuan
  • Wang, Wenzhao

Abstract

The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel machine scheduling problem. Based on this, a corresponding mixed integer programming model is constructed to minimize the makespan and the processing loss beyond the machine RUL threshold, where a discrete teaching and learning based optimization algorithm is applied to solve this NP-hard problem, and a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines. In addition, this paper demonstrates that the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises.

Suggested Citation

  • He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000977
    DOI: 10.1016/j.ress.2022.108429
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022000977
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108429?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hu, Q.P. & Xie, M. & Ng, S.H. & Levitin, G., 2007. "Robust recurrent neural network modeling for software fault detection and correction prediction," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 332-340.
    2. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    3. Li, Tianfu & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Li, Xingyu & Krivtsov, Vasiliy & Arora, Karunesh, 2022. "Attention-based deep survival model for time series data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Wang, Han & Liao, Haitao & Ma, Xiaobing & Bao, Rui, 2021. "Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Zhang, Enze & Wu, Yifei & Chen, Qingwei, 2014. "A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 65-76.
    8. Tamer Abdelmaguid & Mohamed Shalaby & Mohamed Awwad, 2014. "A tabu search approach for proportionate multiprocessor open shop scheduling," Computational Optimization and Applications, Springer, vol. 58(1), pages 187-203, May.
    9. Liao, Guobo & Yin, Hongpeng & Chen, Min & Lin, Zheng, 2021. "Remaining useful life prediction for multi-phase deteriorating process based on Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    10. Robert McNaughton, 1959. "Scheduling with Deadlines and Loss Functions," Management Science, INFORMS, vol. 6(1), pages 1-12, October.
    11. Ouyang, Hai-bin & Gao, Li-qun & Kong, Xiang-yong & Zou, De-xuan & Li, Steven, 2015. "Teaching-learning based optimization with global crossover for global optimization problems," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 533-556.
    12. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Si, Xiao-Sheng & Li, Tianmei & Zhang, Jianxun & Lei, Yaguo, 2022. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Zhang, Yongchao & Zhao, Xiaoli & Sun, Beibei & Liu, Zheng, 2023. "Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Ta, Yuntian & Li, Yanfeng & Cai, Wenan & Zhang, Qianqian & Wang, Zhijian & Dong, Lei & Du, Wenhua, 2023. "Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Zuo, Fei & Zio, Enrico & Xu, Yue, 2023. "Bi-objective optimization of the scheduling of risk-related resources for risk response," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Yang, Ningning & Wang, Zhijian & Cai, Wenan & Li, Yanfeng, 2023. "Data Regeneration Based on Multiple Degradation Processes for Remaining Useful Life Estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Li, Yajing & Wang, Zhijian & Li, Feng & Li, Yanfeng & Zhang, Xiaohong & Shi, Hui & Dong, Lei & Ren, Weibo, 2024. "An ensembled remaining useful life prediction method with data fusion and stage division," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    8. Rasay, Hasan & Taghipour, Sharareh & Sharifi, Mani, 2022. "An integrated Maintenance and Statistical Process Control Model for a Deteriorating Production Process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    9. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Liu, Xingheng & Matias, José & Jäschke, Johannes & Vatn, Jørn, 2022. "Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Zeynep Adak & Mahmure Övül Arıoğlu Akan & Serol Bulkan, 0. "Multiprocessor open shop problem: literature review and future directions," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-23.
    6. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Cai, Xiao & Li, Naipeng & Xie, Min, 2024. "RUL prediction for two-phase degrading systems considering physical damage observations," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. Zeynep Adak & Mahmure Övül Arıoğlu Akan & Serol Bulkan, 2020. "Multiprocessor open shop problem: literature review and future directions," Journal of Combinatorial Optimization, Springer, vol. 40(2), pages 547-569, August.
    13. Zhang, Shuyi & Zhai, Qingqing & Li, Yaqiu, 2023. "Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    14. Pedersen, Tom Ivar & Liu, Xingheng & Vatn, Jørn, 2023. "Maintenance optimization of a system subject to two-stage degradation, hard failure, and imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    15. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    16. Liu Guiqing & Li Kai & Cheng Bayi, 2015. "Preemptive Scheduling with Controllable Processing Times on Parallel Machines," Journal of Systems Science and Information, De Gruyter, vol. 3(1), pages 68-76, February.
    17. Chen, Chuanhai & Li, Bowen & Guo, Jinyan & Liu, Zhifeng & Qi, Baobao & Hua, Chunlei, 2022. "Bearing life prediction method based on the improved FIDES reliability model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    18. Pan, Yan & Liang, Bin & Yang, Lei & Liu, Houde & Wu, Tonghai & Wang, Shuo, 2024. "Spatial-temporal modeling of oil condition monitoring: A review," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    19. Liang, Pengfei & Tian, Jiaye & Wang, Suiyan & Yuan, Xiaoming, 2024. "Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    20. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000977. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.